This is a notebook to analyse french arvor-rbr floats first data

Data from those floats are automatically downloaded in the repo by a github action, so we can use localftp source from argopy to check the data.
Here we work with argopy (https://argopy.readthedocs.io), so with xarray datasets

author = "kevin.balem@ifremer.fr"

from argopy import IndexFetcher, DataFetcher
import matplotlib.pyplot as plt
import cmocean as cm
from matplotlib.ticker import MaxNLocator
from matplotlib.lines import Line2D
import cartopy.crs as ccrs
plt.rcParams['axes.grid'] = True
import numpy as np
import gsw
import xarray as xr
import pandas as pd

/opt/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm

wmos = [6903075,6903076]

Float trajectories

IndexFetcher(src='erddap',cache=False).float(wmos).plot()

(<Figure size 720x720 with 1 Axes>,
 <cartopy.mpl.geoaxes.GeoAxesSubplot at 0x7f9c9a676be0>)

Get data & plot profiles

# GET DATA FROM REPO DATA COPY (cache=False because new profiles are added to the repo)
ds = DataFetcher(cache=False,src='localftp',local_ftp='/export/home/kbalem/Bureau/earise-rbr/data/floats/ftp',mode='expert').float(wmos).to_xarray()
# CALCULATE TEOS10 VARIABLE
ds.argo.teos10(['SA','SIG0','PTEMP'])
# TURN TO PROFILES
ds = ds.argo.point2profile()
ds0=ds.where(ds['PLATFORM_NUMBER']==wmos[0],drop=True) 
ds1=ds.where(ds['PLATFORM_NUMBER']==wmos[1],drop=True) 

def full_profiles(ds0=ds0,ds1=ds1,markersize=1):
    fig,ax = plt.subplots(len(wmos),2,figsize=(16,len(wmos)*8),sharey=True)
    ax[0,0].invert_yaxis();
    ax[0,0].plot(ds0['TEMP'],-gsw.z_from_p(ds0['PRES'],ds0['LATITUDE'].mean()),'.k',markersize=markersize)
    ax[0,1].plot(ds0['PSAL'],-gsw.z_from_p(ds0['PRES'],ds0['LATITUDE'].mean()),'.k',markersize=markersize)    
    ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                            label='RBR Floats',markerfacecolor='k', markersize=10),
                            ],loc='upper right', bbox_to_anchor=(1.3, 1))
    ax[0,0].set_ylabel('Depth',fontsize='large',fontweight='bold')
    a=ax[0,1].text(1.05,0.4,'float '+str(wmos[0]),rotation="vertical",transform = ax[0,1].transAxes, fontsize='large', fontweight='bold')
    ax[0,0].set_xlabel('Temperature',fontsize='large',fontweight='bold')
    ax[0,1].set_xlabel('Salinity',fontsize='large',fontweight='bold')
    ax[1,0].plot(ds1['TEMP'],-gsw.z_from_p(ds1['PRES'],ds1['LATITUDE'].mean()),'.k',markersize=markersize)
    ax[1,1].plot(ds1['PSAL'],-gsw.z_from_p(ds1['PRES'],ds1['LATITUDE'].mean()),'.k',markersize=markersize)        
    ax[1,0].set_ylabel('Depth',fontsize='large',fontweight='bold')
    a=ax[1,1].text(1.05,0.4,'float '+str(wmos[1]),rotation="vertical",transform = ax[1,1].transAxes, fontsize='large', fontweight='bold')
    ax[1,0].set_xlabel('Temperature',fontsize='large',fontweight='bold')
    ax[1,1].set_xlabel('Salinity',fontsize='large',fontweight='bold')       
    ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                            label='RBR Floats',markerfacecolor='k', markersize=10),
                            ],loc='upper right', bbox_to_anchor=(1.3, 1))
    return fig,ax
fig,ax = full_profiles(ds0=ds0.isel(N_PROF=1),ds1=ds1.isel(N_PROF=1))    

Let's plot TS diagram for all profiles

# TS DIAGRAM
def full_ts(ds0=ds0,ds1=ds1,vmin=25.95,vmax=27.8,sigma=8):    
    mint=ds['PTEMP'].min()
    maxt=ds['PTEMP'].max()
    mins=ds['SA'].min()
    maxs=ds['SA'].max()
    tempL=np.linspace(mint-1,maxt+1,156)
    salL=np.linspace(mins-1,maxs+1,156)
    Tg, Sg = np.meshgrid(tempL,salL)
    sigma_theta = gsw.sigma0(Sg, Tg)

    fig,ax=plt.subplots(1,2,figsize=(25,10))

    cs = ax[0].contour(Sg, Tg, sigma_theta,sigma, colors='grey', zorder=1)
    cl=ax[0].clabel(cs,fontsize=10,inline=False,fmt='%.1f')
    sc=ax[0].scatter(ds0['SA'],ds0['PTEMP'],c=ds0['SIG0'],s=10,cmap=cm.cm.matter,vmin=vmin,vmax=vmax)

    ax[0].set_xlabel('Salinity ($‰$)')
    ax[0].set_ylabel('Temperature[$^\circ$C]')
    ax[0].set_title(str(wmos[0]),fontsize=14, fontweight='bold')
    ax[0].xaxis.set_major_locator(MaxNLocator(nbins=6))
    ax[0].yaxis.set_major_locator(MaxNLocator(nbins=8))
    ax[0].tick_params(direction='out')

    cs = ax[1].contour(Sg, Tg, sigma_theta, sigma, colors='grey', zorder=1)
    cl=ax[1].clabel(cs,fontsize=10,inline=False,fmt='%.1f')
    sc=ax[1].scatter(ds1['SA'],ds1['PTEMP'],c=ds1['SIG0'],s=10,cmap=cm.cm.matter,vmin=vmin,vmax=vmax)

    ax[1].set_xlabel('Salinity ($‰$)')
    ax[1].set_ylabel('Temperature[$^\circ$C]')
    ax[1].set_title(str(wmos[1]),fontsize=14, fontweight='bold')
    ax[1].xaxis.set_major_locator(MaxNLocator(nbins=6))
    ax[1].yaxis.set_major_locator(MaxNLocator(nbins=8))
    ax[1].tick_params(direction='out')

    cb=plt.colorbar(sc,ax=ax.ravel().tolist())
    cb.ax.tick_params(direction='out')
    cb.set_label('Density[kg m$^{-3}$]')

    ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10)],loc='lower right')
    ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10)],loc='lower right')
    return fig,ax
fig,ax = full_ts()    

 Comparison with woa

# WOA OPENDAP URL FOR 2005-2017, GLOBAL 1/4°
woa_temp='https://www.ncei.noaa.gov/thredds-ocean/dodsC/ncei/woa/temperature/A5B7/0.25/woa18_A5B7_t13_04.nc'
woa_psal='https://www.ncei.noaa.gov/thredds-ocean/dodsC/ncei/woa/salinity/A5B7/0.25/woa18_A5B7_s13_04.nc'

# LOAD DATASETS, No need to decode times for a clim
woat = xr.open_dataset(woa_temp,decode_times=False)
woas = xr.open_dataset(woa_psal,decode_times=False)

# INTERPOLATE WOA T/S/SIG TO FLOAT POSITION
woat_float0 = woat['t_mn'].interp(lat=ds0['LATITUDE'],lon=ds0['LONGITUDE'],method='linear').squeeze().to_dataset()
woas_float0 = woas['s_mn'].interp(lat=ds0['LATITUDE'],lon=ds0['LONGITUDE'],method='linear').squeeze().to_dataset()
woa_float0 = xr.merge([woat_float0,woas_float0])

woat_float1 = woat['t_mn'].interp(lat=ds1['LATITUDE'],lon=ds1['LONGITUDE'],method='linear').squeeze().to_dataset()
woas_float1 = woas['s_mn'].interp(lat=ds1['LATITUDE'],lon=ds1['LONGITUDE'],method='linear').squeeze().to_dataset()
woa_float1 = xr.merge([woat_float1,woas_float1])

# Reduce depth range
woa_float0 = woa_float0.sel(depth=slice(0,2000))
woa_float1 = woa_float1.sel(depth=slice(0,2000))

fig,ax=full_profiles()
ax[0,0].plot(woa_float0['t_mn'],woa_float0['depth'],'.r',markersize=4)         
ax[0,1].plot(woa_float0['s_mn'],woa_float0['depth'],'.r',markersize=4)       
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
ax[1,0].plot(woa_float1['t_mn'],woa_float1['depth'],'.r',linewidth=1)         
ax[1,1].plot(woa_float1['s_mn'],woa_float1['depth'],'.r')       
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))

<matplotlib.legend.Legend at 0x7f9c77756e10>

# ZOOM
fig,ax=full_profiles(markersize=4)
ax[0,0].set_ylim(2000,1500)
ax[0,0].plot(woa_float0['t_mn'],woa_float0['depth'],'.r',markersize=8)         
ax[0,1].plot(woa_float0['s_mn'],woa_float0['depth'],'.r',markersize=8)       
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
ax[1,0].plot(woa_float1['t_mn'],woa_float1['depth'],'.r',linewidth=1)         
ax[1,1].plot(woa_float1['s_mn'],woa_float1['depth'],'.r')       
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))

ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);

# GSW 
pr0 = np.empty([len(woa_float0['depth']),len(woa_float0['N_PROF'])])
pr1 = np.empty([len(woa_float1['depth']),len(woa_float1['N_PROF'])])

for i in range(len(woa_float0['N_PROF'])):
    pr0[:,i]=gsw.p_from_z(-woa_float0['depth'],woa_float0['LATITUDE'].isel(N_PROF=i))
woa_float0['PRES']=xr.DataArray(pr0,dims=['depth','N_PROF'])   
woa_float0['SA']=xr.DataArray(gsw.SA_from_SP(woa_float0['s_mn'],woa_float0['PRES'],woa_float0['LONGITUDE'],woa_float0['LATITUDE']),dims=['depth','N_PROF'])
woa_float0['PTEMP']=xr.DataArray(gsw.pt0_from_t(woa_float0['SA'],woa_float0['t_mn'],woa_float0['PRES']),dims=['depth','N_PROF'])
woa_float0['SIG0']=xr.DataArray(gsw.sigma0(woa_float0['SA'], woa_float0['PTEMP']),dims=['depth','N_PROF'])

for i in range(len(woa_float1['N_PROF'])):
    pr0[:,i]=gsw.p_from_z(-woa_float1['depth'],woa_float1['LATITUDE'].isel(N_PROF=i))
woa_float1['PRES']=xr.DataArray(pr0,dims=['depth','N_PROF'])   
woa_float1['SA']=xr.DataArray(gsw.SA_from_SP(woa_float1['s_mn'],woa_float1['PRES'],woa_float1['LONGITUDE'],woa_float1['LATITUDE']),dims=['depth','N_PROF'])
woa_float1['PTEMP']=xr.DataArray(gsw.pt0_from_t(woa_float1['SA'],woa_float1['t_mn'],woa_float1['PRES']),dims=['depth','N_PROF'])
woa_float1['SIG0']=xr.DataArray(gsw.sigma0(woa_float1['SA'], woa_float1['PTEMP']),dims=['depth','N_PROF'])
woa_float1

<xarray.Dataset>
Dimensions:    (N_PROF: 20, depth: 67)
Coordinates:
  * N_PROF     (N_PROF) int64 0 21 22 23 24 25 26 27 ... 32 33 34 35 36 37 38 39
  * depth      (depth) float64 0.0 5.0 10.0 15.0 ... 1.9e+03 1.95e+03 2e+03
    time       float32 73.5
    lat        (N_PROF) float64 29.17 29.15 29.1 29.09 ... 30.41 30.57 30.56
    lon        (N_PROF) float64 -19.0 -19.02 -19.12 ... -18.55 -18.43 -18.29
    TIME       (N_PROF) datetime64[ns] 2020-12-11T10:25:00 ... 2021-06-10T05:...
    LONGITUDE  (N_PROF) float64 -19.0 -19.02 -19.12 ... -18.55 -18.43 -18.29
    LATITUDE   (N_PROF) float64 29.17 29.15 29.1 29.09 ... 30.41 30.57 30.56
Data variables:
    t_mn       (depth, N_PROF) float64 nan nan nan nan nan ... nan nan nan nan
    s_mn       (depth, N_PROF) float64 36.85 36.85 nan nan ... nan nan nan nan
    PRES       (depth, N_PROF) float64 0.0 0.0 0.0 ... 2.024e+03 2.024e+03
    SA         (depth, N_PROF) float64 37.02 37.02 nan nan ... nan nan nan nan
    PTEMP      (depth, N_PROF) float64 nan nan nan nan nan ... nan nan nan nan
    SIG0       (depth, N_PROF) float64 nan nan nan nan nan ... nan nan nan nan

# TS DIAGRAM
fig,ax = full_ts()
sc_b=ax[0].scatter(woa_float0['SA'],woa_float0['PTEMP'],c=woa_float0['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k')
sc_b=ax[1].scatter(woa_float1['SA'],woa_float1['PTEMP'],c=woa_float1['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k')

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c740686a0>

# ZOOM
fig,ax = full_ts(vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)
sc_b=ax[0].scatter(woa_float0['SA'],woa_float0['PTEMP'],c=woa_float0['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k')
sc_b=ax[1].scatter(woa_float1['SA'],woa_float1['PTEMP'],c=woa_float1['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k')

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c73ddaac8>

Comparison with argo profiles nearby

plt.figure()
# FETCH PROFILES META IN OUR REGION
IndexFetcher(cache=True,src='erddap').region([-19.8,-18.8,29.0,30.0]).plot()
# RBR PROFILES
plt.plot(ds['LONGITUDE'],ds['LATITUDE'],'k*',markersize=10)

[<matplotlib.lines.Line2D at 0x7f9c9a6d64e0>]

<Figure size 432x288 with 0 Axes>

# GET DATA FROM ERDDAP, in standard mode to automatically keep good data
px = DataFetcher(cache=True,src='erddap',mode='standard').region([-19.8,-18.8,29.0,30.0,0.,2000.]).to_xarray()
# CALCULATE TEOS10 VARIABLE
px.argo.teos10(['SA','SIG0','PTEMP'])
# TURN TO PROFILES
px = px.argo.point2profile()
# Keep only december to may profiles
px['MONTH']=xr.DataArray(np.array(pd.DatetimeIndex(px['TIME'].values).month),dims='N_PROF')
px = px.where((px['MONTH']<=5)|(px['MONTH']==12), drop=True)
px

<xarray.Dataset>
Dimensions:                (N_LEVELS: 1018, N_PROF: 59)
Coordinates:
  * N_PROF                 (N_PROF) int64 1 2 12 14 15 ... 106 107 108 109 110
  * N_LEVELS               (N_LEVELS) int64 0 1 2 3 4 ... 1014 1015 1016 1017
    TIME                   (N_PROF) datetime64[ns] 2002-05-14T05:00:00 ... 20...
    LONGITUDE              (N_PROF) float64 -19.3 -19.23 ... -19.48 -18.96
    LATITUDE               (N_PROF) float64 29.08 29.31 29.78 ... 29.51 29.58
Data variables:
    CONFIG_MISSION_NUMBER  (N_PROF) float64 2.0 2.0 2.0 1.0 ... 2.0 2.0 2.0 2.0
    CYCLE_NUMBER           (N_PROF) float64 5.0 6.0 60.0 6.0 ... 13.0 14.0 15.0
    DATA_MODE              (N_PROF) object 'D' 'D' 'D' 'D' ... 'R' 'R' 'R' 'R'
    DIRECTION              (N_PROF) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER        (N_PROF) float64 1.9e+06 1.9e+06 ... 6.903e+06
    POSITION_QC            (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    PRES                   (N_PROF, N_LEVELS) float32 15.0 25.0 35.0 ... nan nan
    PRES_QC                (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    PSAL                   (N_PROF, N_LEVELS) float32 36.873 36.869 ... nan nan
    PSAL_QC                (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    PTEMP                  (N_PROF, N_LEVELS) float64 19.41 19.41 ... nan nan
    SA                     (N_PROF, N_LEVELS) float64 37.05 37.04 ... nan nan
    SIG0                   (N_PROF, N_LEVELS) float64 26.35 26.35 ... nan nan
    TEMP                   (N_PROF, N_LEVELS) float32 19.41 19.41 ... nan nan
    TEMP_QC                (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    TIME_QC                (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    MONTH                  (N_PROF) float64 5.0 5.0 3.0 12.0 ... 2.0 4.0 4.0 5.0
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         https://www.ifremer.fr/erddap
    Fetched_by:           kbalem
    Fetched_date:         2021/06/15
    Fetched_constraints:  [x=-19.80/-18.80; y=29.00/30.00; z=0.0/2000.0]
    Fetched_uri:          ['https://www.ifremer.fr/erddap/tabledap/ArgoFloats...
    history:              Variables filtered according to DATA_MODE; Variable...

ds0b=ds0.isel(N_PROF=[0,1,2,3,4,5,6,7,8])
ds1b=ds1.isel(N_PROF=[0,1,2,3,4,5,6,7,8])

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b)

ax[0,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)
ax[0,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
 
ax[1,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)   
ax[1,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))

<matplotlib.legend.Legend at 0x7f9c712f2fd0>

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b,markersize=4)
ax[0,0].set_ylim(2000,1500)

ax[0,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)
ax[0,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
 
ax[1,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)   
ax[1,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
   
ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);

# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b)

sc_b=ax[0].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',zorder=0)
sc_b=ax[1].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',zorder=0)

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c6682fc18>

# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b,vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)
sc_b=ax[0].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',zorder=0)
sc_b=ax[1].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',zorder=0)
ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c66566a20>

 Comparison of 1sts profiles with RAPROCEAN2012 CTD Cast

st25=pd.read_csv('../data/ref/RAPROCAN2012/Station25.csv',header=4,sep=';',names=['PRES','PSAL','TEMP'])
mt25={'latitude':29.17,'longitude':-19.00,'time':np.datetime64('2020-12-10T17:41:05')}

st25['SA'] = gsw.SA_from_SP(st25['PSAL'],st25['PRES'],mt25['longitude'],mt25['latitude'])
st25['PTEMP'] = gsw.pt0_from_t(st25['SA'],st25['TEMP'],st25['PRES'])
st25['SIG0'] = gsw.sigma0(st25['SA'], st25['PTEMP'])
st25.head()

	PRES	PSAL	TEMP	SA	PTEMP	SIG0
0	8.0	36.9540	21.3449	37.128475	21.343339	25.870281
1	10.0	36.9541	21.3478	37.128578	21.345849	25.869658
2	12.0	36.9539	21.3508	37.128373	21.348458	25.868777
3	14.0	36.9539	21.3484	37.128369	21.345668	25.869552
4	16.0	36.9543	21.3442	37.128767	21.341078	25.871130

# LET's KEEP ONLY THE 1st PROFILES
ds0b=ds0.isel(N_PROF=[0,1,2,3])
ds1b=ds1.isel(N_PROF=[0,1,2,3])

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b)
ax[0,0].set_ylim([2000,0]); 
ax[0,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)
ax[0,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
ax[1,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)
ax[1,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c6b8225c0>

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b,markersize=4)

ax[0,0].set_ylim([2000,1500]); 
ax[0,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)
ax[0,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
ax[1,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)
ax[1,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')

ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);

# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b)
sc_b=ax[0].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)
ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c680198d0>

# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b,vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)
sc_b=ax[0].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)
ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c67d044a8>

 SBE 6903010 float comparison

# GET DATA FROM REPO DATA COPY (cache=False because new profiles are added to the repo)
dsbe = DataFetcher(cache=False,src='erddap',mode='expert').float(6903010).to_xarray()
# CALCULATE TEOS10 VARIABLE
dsbe.argo.teos10(['SA','SIG0','PTEMP'])
# TURN TO PROFILES
dsbe = dsbe.argo.point2profile()
dsbe

<xarray.Dataset>
Dimensions:                   (N_LEVELS: 1018, N_PROF: 20)
Coordinates:
  * N_LEVELS                  (N_LEVELS) int64 0 1 2 3 4 ... 1014 1015 1016 1017
    TIME                      (N_PROF) datetime64[ns] 2020-12-10T18:26:00 ......
    LONGITUDE                 (N_PROF) float64 -19.01 -19.0 ... -18.82 -18.73
    LATITUDE                  (N_PROF) float64 29.17 29.15 29.13 ... 30.8 30.97
  * N_PROF                    (N_PROF) int64 0 1 2 3 4 5 6 ... 14 15 16 17 18 19
Data variables:
    CONFIG_MISSION_NUMBER     (N_PROF) int64 1 1 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2
    CYCLE_NUMBER              (N_PROF) int64 1 1 2 3 4 5 6 ... 14 15 16 17 18 19
    DATA_MODE                 (N_PROF) <U1 'R' 'R' 'R' 'R' ... 'R' 'R' 'R' 'R'
    DIRECTION                 (N_PROF) <U1 'D' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER           (N_PROF) int64 6903010 6903010 ... 6903010 6903010
    POSITION_QC               (N_PROF) int64 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    PRES                      (N_PROF, N_LEVELS) float32 8.7 10.4 ... nan nan
    PRES_ADJUSTED             (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    PRES_ADJUSTED_ERROR       (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    PRES_ADJUSTED_QC          (N_PROF) int64 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
    PRES_QC                   (N_PROF) int64 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    PSAL                      (N_PROF, N_LEVELS) float32 36.946 36.947 ... nan
    PSAL_ADJUSTED             (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    PSAL_ADJUSTED_ERROR       (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    PSAL_ADJUSTED_QC          (N_PROF) int64 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
    PSAL_QC                   (N_PROF, N_LEVELS) int64 1 1 1 ... 99999 99999
    PTEMP                     (N_PROF, N_LEVELS) float64 21.32 21.32 ... nan nan
    SA                        (N_PROF, N_LEVELS) float64 37.12 37.12 ... nan nan
    SIG0                      (N_PROF, N_LEVELS) float64 25.89 25.89 ... nan nan
    TEMP                      (N_PROF, N_LEVELS) float32 21.32 21.325 ... nan
    TEMP_ADJUSTED             (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    TEMP_ADJUSTED_ERROR       (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    TEMP_ADJUSTED_QC          (N_PROF) int64 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
    TEMP_QC                   (N_PROF, N_LEVELS) int64 1 1 1 ... 99999 99999
    TIME_QC                   (N_PROF) int64 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    VERTICAL_SAMPLING_SCHEME  (N_PROF) <U207 'Primary sampling: averaged [10 ...
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         https://www.ifremer.fr/erddap
    Fetched_by:           kbalem
    Fetched_date:         2021/06/15
    Fetched_constraints:  phy;WMO6903010
    Fetched_uri:          ['https://www.ifremer.fr/erddap/tabledap/ArgoFloats...
    history:              Transformed with point2profile

fig=plt.figure(figsize=(10,6))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
plt.plot(dsbe['LONGITUDE'],dsbe['LATITUDE'],'ro-',label='SBE 6903010')
plt.plot(ds0['LONGITUDE'],ds0['LATITUDE'],'bo-',label='RBR 6903075')
plt.plot(ds1['LONGITUDE'],ds1['LATITUDE'],'go-',label='RBR 6903076')
plt.xlim([-19.3,-18.9])
plt.ylim([29,29.2])
plt.legend()

<matplotlib.legend.Legend at 0x7f9c67aa2668>

We'll plot only until CYCLE 10, after that profiles positions differ a bit

ds0b=ds0.isel(N_PROF=[0,1,2,3,4,5,6,7,8,9])
ds1b=ds1.isel(N_PROF=[0,1,2,3,4,5,6,7,8,9])
dsbe0=dsbe.isel(N_PROF=[0,1,2,3,4,5,6,7,8,9])

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b)
ax[0,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
ax[0,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
    
ax[1,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
ax[1,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
    
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c5ec63278>

# ZOOM
fig,ax = full_profiles(ds0=ds0b,ds1=ds1b,markersize=4)

ax[0,0].set_ylim([2000,1500]); 
ax[0,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
ax[0,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
    
ax[1,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
ax[1,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
    
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
    
ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);

# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b)
sc_b=ax[0].scatter(dsbe0['SA'],dsbe0['PTEMP'],c=dsbe0['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(dsbe0['SA'],dsbe0['PTEMP'],c=dsbe0['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c54196fd0>

# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b,vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)

sc_b=ax[0].scatter(ds1b['SA'],ds1b['PTEMP'],c=ds1b['SIG0'],s=10,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(ds1b['SA'],ds1b['PTEMP'],c=ds1b['SIG0'],s=10,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f9c53f09d30>

Difference profile with reference station on theta levels

from wrangling import interp_climatology

ref_sal = st25['SA'].values
ref_theta = st25['PTEMP'].values
ref_pres = st25['PRES'].values

#INTERP REF STATION ON 6903075 THETA LEVELS 
SA_i0, PR_i0 = interp_climatology(np.array([ds0.isel(N_PROF=1)['SA'].values,]*2).transpose(), 
                                  np.array([ds0.isel(N_PROF=1)['PTEMP'].values,]*2).transpose(), 
                                  np.array([ds0.isel(N_PROF=1)['PRES'].values,]*2).transpose(), 
                                  ref_sal, ref_theta, ref_pres)
SA_i0 = SA_i0[:,0]
PR_i0 = PR_i0[:,0]

#INTERP 6903076 ON 6903075 THETA LEVELS  
SA_i1, PR_i1 = interp_climatology(np.array([ds1.isel(N_PROF=1)['SA'].values,]*2).transpose(), 
                                  np.array([ds1.isel(N_PROF=1)['PTEMP'].values,]*2).transpose(), 
                                  np.array([ds1.isel(N_PROF=1)['PRES'].values,]*2).transpose(),
                                  ref_sal, ref_theta, ref_pres)
SA_i1 = SA_i1[:,0]
PR_i1 = PR_i1[:,0]

#INTERP 6903010 ON 6903075 THETA LEVELS  
SA_i2, PR_i2 = interp_climatology(np.array([dsbe.isel(N_PROF=1)['SA'].values,]*2).transpose(), 
                                  np.array([dsbe.isel(N_PROF=1)['PTEMP'].values,]*2).transpose(), 
                                  np.array([dsbe.isel(N_PROF=1)['PRES'].values,]*2).transpose(),
                                  ref_sal, ref_theta, ref_pres)
SA_i2 = SA_i2[:,0]
PR_i2 = PR_i2[:,0]

fig,ax = plt.subplots(1,2,figsize=(15,10),sharey=True)
ax[0].invert_yaxis();

ax[0].plot(SA_i0 - ref_sal, ref_pres,label='6903075')
ax[0].plot(SA_i1 - ref_sal, ref_pres,label='6903076')
ax[0].plot(SA_i2 - ref_sal, ref_pres,label='6903010')

ax[0].legend()
ax[0].set_xlabel('Delta Salinity with St25',fontsize='large',fontweight='bold')
ax[0].set_ylabel('PRES',fontsize='large',fontweight='bold')

ax[1].plot(PR_i0 - ref_pres, ref_pres,label='6903075')
ax[1].plot(PR_i1 - ref_pres, ref_pres,label='6903076')
ax[1].plot(PR_i2 - ref_pres, ref_pres,label='6903010')

ax[1].legend()
ax[1].set_xlabel('Delta Pressure with St25',fontsize='large',fontweight='bold')

plt.tight_layout()

Evolution of pres/salinity along an isotherm

def along_isot(isotherm):

    fig,ax = plt.subplots(2,1,figsize=(18,12),sharex=True)
    ds0_ai = ds0.where((ds0['PTEMP']<isotherm+.1)&(ds0['PTEMP']>isotherm-.1),drop=True).mean('N_LEVELS')
    ds1_ai = ds1.where((ds1['PTEMP']<isotherm+.1)&(ds1['PTEMP']>isotherm-.1),drop=True).mean('N_LEVELS')
    dsbe_ai = dsbe.where((dsbe['PTEMP']<isotherm+.1)&(dsbe['PTEMP']>isotherm-.1),drop=True).mean('N_LEVELS')
    ax[0].plot(ds0_ai['CYCLE_NUMBER'],ds0_ai['PRES'],'o-',label=str(wmos[0]))
    ax[0].plot(ds1_ai['CYCLE_NUMBER'],ds1_ai['PRES'],'o-',label=str(wmos[1]))
    ax[0].plot(dsbe_ai['CYCLE_NUMBER'],dsbe_ai['PRES'],'o-',label='sbe 6903010')
    #ax[0].set_xlabel('CYCLE')
    ax[0].set_ylabel('PRES')
    ax[0].legend()
    ax[0].set_title('Pressure along isotherm '+str(isotherm)+'°C')

    ax[1].plot(ds0_ai['CYCLE_NUMBER'],ds0_ai['SA'],'o-',label=str(wmos[0]))
    ax[1].plot(ds1_ai['CYCLE_NUMBER'],ds1_ai['SA'],'o-',label=str(wmos[1]))
    ax[1].plot(dsbe_ai['CYCLE_NUMBER'],dsbe_ai['SA'],'o-',label='sbe 6903010')
    ax[1].set_xlabel('CYCLE')
    ax[1].set_ylabel('Salinity')
    ax[1].legend()
    ax[1].set_title('Salinity along isotherm '+str(isotherm)+'°C')
    return fig,ax

for i in [10.,5.]:
    fig,ax = along_isot(i)       

/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)

Notes

Virginie :

Le 6903076 et le 6903010 sont proches en salinité le 6903075 est plus salé que les 2 autres mais plus proche de la station de référence.

donc le 6903076 et le 6903010 sous-estiment la salinité. il faudrait faire tourner OW sur le SBE 6903010 pour confirmer.

Objs :

• sortir les flotteurs de la greylist
• evaluer RBR

Appliquer OWC sur les 3 flotteurs

Comparaison sur niveaux theta : profil de différence entre flotteurs/CTD sur niveaux theta

Envoyer données à M.Dever (WHOI)